Inside the “black box”: Embedding clinical knowledge in data-driven machine learning for heart disease diagnosis

ABSTRACT:

Background

Ischemic heart disease (IHD) caused by the narrowing of coronary arteries is a major cause of morbidity and mortality worldwide. Clinical diagnosis involves complex, costly, and potentially invasive procedures.

Objective

To address this problem, we introduce a novel clinical knowledge-enhanced machine learning (ML) pipeline to assist in timely and cost-effective IHD prediction.

Methods

Unlike conventional data-driven “black box” ML approaches, we propose an effective mechanism to engage clinical expertise and gain insight into the “black box” at each stage of model development, including data analysis, preprocessing, selecting the most clinically discriminative features, and model evaluation. One-hot feature encoding is introduced to expose hidden bias

SUBMITTER: Meng J 

PROVIDER: S-EPMC9795264 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature